Cooperative search and tracking of multiple Unmanned Aerial Vehicle(UAV) systems is becoming a key application paradigm for realizing efficient coordination missions. This paper proposes a multiple UAV cooperative search and tracking framework. This framework includes four components: task assignment, motion planning, target detection and target tracking. Among them, task assignment uses auction algorithm to solve the distributed task assignment problem; motion planning uses deep reinforcement learning algorithm; target detection uses YOLOv8 algorithm and ByteTrack algorithm to achieve target recognition and tracking on the image plane; and target tracking utilizes position-based visual servo control to achieve target tracking control of UAV in space. We experimentally validate our proposed framework through XTDrone simulator. The experimental results show that each functional module in our framework plays its corresponding role and can effectively improve the search and tracking efficiency in complex environments of UAVs.